IP Library Granted Patent US 12685244
Granted Patent B2
US 12685244 · App. 17/975,996 · Granted Jul 21, 2026

Apparatus and method for providing wide-area precision agriculture service based on collaboration between heterogeneous drones

Inventors: Yang-Jae Jeong (Incheon, KR); Kyung-Il Kim (Daejeon, KR); Chae-Deok Lim (Daejeon, KR); Beob-Kyun Kim (Jeonju-si, KR); Young-Bin Kim (Daejeon, KR); Jin-Ah Shin (Daejeon, KR); Duk-Kyun Woo (Daejeon, KR); Dong-Wan Ryoo (Daejeon, KR); Yoo-Jin Lim (Sejong-si, KR); Su-Jung Ha (Daejeon, KR)
Assignee: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE
A01B79/005A01C21/005B64U10/25B64U2101/30B64U2201/10
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Quick Facts
Patent No.
US 12685244
App. No.
17/975,996
Granted
Jul 21, 2026
Kind
B2
Abstract

Disclosed herein are an apparatus and method for providing a wide-area precision agriculture service based on collaboration between heterogeneous drones. The method for providing a wide-area precision agriculture service based on collaboration between heterogeneous drones includes transferring first mission information including photography of an entirety of arable land to a fixed-wing drone, receiving first drone data corresponding to the first mission information from the fixed-wing drone, and analyzing the entire agricultural arable land based on the first drone data, and transferring detailed mission information generated based on a result of analysis of the entire agricultural arable land to at least one rotary-wing drone.

Claims (63)

1 . A method for providing a service based on collaboration between heterogeneous devices, comprising:

collecting data captured or generated by the heterogeneous devices which form a distributed system across multiple locations; and

performing processing and management activities conducted on the data including at least one of data monitoring, analysis, and combination to produce an output for the service,

wherein the data monitoring is the process of monitoring service information after reconstruction of one precise large-scale image from sequential capturing of real-time data with data coordination,

wherein the devices include a fixed-wing drone and at least one rotary-wing drone, and

wherein the method further comprises:

transferring first mission information including photo shooting mission for the entire agriculture arable land to a fixed-wing drone;

receiving first drone data corresponding to the first mission information from the fixed-wing drone, and analyzing the entire agricultural arable land based on the first drone data; and

transferring detailed mission information generated based on a result of analysis of the entire agricultural arable land to at least one rotary-wing drone.

2 . The method of claim 1 ,

wherein the first mission information includes information about a location of the entire agricultural arable land, a flight path, photo shooting plan and information about missions for respective periods depending on lifecycles of respective crops,

wherein analyzing the entire agricultural arable land comprises:

calling an Artificial Intelligence (AI) model mapped to the first mission information; and

inferring a result of analysis, including a distribution of crops over the entire agricultural arable land and growth states of respective crops, from the first drone data based on the called AI model.

3 . The method of claim 1 , wherein:

transferring to the at least one rotary-wing drone comprises:

transferring second mission information for precision monitoring of a problem region, identified based on the result of analysis of the entire agricultural arable land, to a first rotary-wing drone, and

the method further comprises:

receiving second drone data corresponding to the second mission information from the first rotary-wing drone and precisely analyzing the problem region based on the second drone data.

4 . The method of claim 3 , wherein:

the first rotary-wing drone comprises multiple rotary-wing drones, each mapped to a unit arable land partition on which one kind of crop is cultivated, and

the second mission information differs for each unit arable land partition.

5 . The method of claim 3 , wherein precisely analyzing the problem region comprises:

calling an Artificial Intelligence (AI) model mapped to the second mission information; and

inferring a result of analysis including growth states of individual crops cultivated on respective unit arable land partitions from the second drone data based on the called AI model.

6 . The method of claim 3 , wherein:

the result of analysis of the entire agricultural arable land and a result of precision analysis of the problem region are managed, together with the mission information and area location information, as topographic information, and

the topographic information is used to generate detailed mission information.

7 . The method of claim 3 , wherein transferring to the at least one rotary-wing drone comprises:

transferring third mission information including performance of a detailed task, generated based on at least one of the result of analysis of the entire agricultural arable land or a result of precision analysis of the problem region or a combination thereof, to a second rotary-wing drone.

8 . The method of claim 1 , wherein the service information is managed by the data monitoring for management of data acquired from the heterogeneous devices in real time.

9 . An apparatus for providing a service based on collaboration between heterogeneous devices, comprising:

a memory for storing at least one program; and

a processor for executing the program,

wherein the program is configured to perform:

collecting data captured or generated by the heterogeneous devices which form a distributed system across multiple locations; and

performing processing and management activities conducted on the data including at least one of data monitoring, analysis, and combination to produce an output for the service,

wherein the data monitoring is the process of monitoring service information after reconstruction of one precise large-scale image from sequential capturing of real-time data with data coordination,

wherein the devices include a fixed-wing drone and at least one rotary-wing drone, and

wherein the program is configured to perform:

transferring first mission information including photo shooting mission for the entire farmland to a fixed-wing drone,

receiving first drone data corresponding to the first mission information from the fixed-wing drone, and analyzing the entire agricultural arable land based on the first drone data, and

transferring detailed mission information generated based on a result of analysis of the entire agricultural arable land to at least one rotary-wing drone.

10 . The apparatus of claim 9 , wherein:

the first mission information includes information about a location of the entire agricultural arable land, a flight path, photo shooting plan and information about missions for respective periods depending on lifecycles of respective crops, and

the program is configured to perform, in analyzing the entire agricultural arable land;

calling an Artificial Intelligence (AI) model mapped to the first mission information, and

inferring a result of analysis, including a distribution of crops of the entire agricultural arable land and growth states of respective crops, from the first drone data based on the called AI model.

11 . The apparatus of claim 9 , wherein:

the program is configured to perform, in transferring to the at least one rotary-wing drone, transferring second mission information for precision monitoring of a problem region, identified based on the result of analysis of the entire agricultural arable land, to a first rotary-wing drone, and

the program is configured to further perform, receiving second drone data corresponding to the second mission information from the first rotary-wing drone and precisely analyzing the problem region based on the second drone data.

12 . The apparatus of claim 11 , wherein:

the first rotary-wing drone comprises multiple rotary-wing drones, each mapped to a unit arable land partition on which one kind of crop is cultivated, and

the second mission information differs for each unit arable land partition.

13 . The apparatus of claim 11 , wherein the program is configured to perform, in precisely analyzing the problem region:

calling an Artificial Intelligence (AI) model mapped to the second mission information, and

inferring a result of analysis including growth states of individual crops cultivated on respective unit arable land partitions from the second drone data based on the called AI model.

14 . The apparatus of claim 11 , wherein:

the program is configured to manage the result of analysis of the entire agricultural arable land and a result of precision analysis of the problem region, together with the mission information and area location information, as topographic information, and

the topographic information is used to generate detailed mission information.

15 . The apparatus of claim 11 , wherein the program is configured to perform, in transferring to the at least one rotary-wing drone, transferring third mission information including performance of a detailed task, generated based on at least one of the result of analysis of the entire agricultural arable land or a result of precision analysis of the problem region or a combination thereof, to a second rotary-wing drone.

16 . The apparatus of claim 9 , wherein:

wherein the service information is managed by the data monitoring for systematic management of data acquired from the heterogeneous devices in real time.